PARALLEL MODEL COMBINATION FOR SPEECH RECOGNITION IN ADDITIVE AND CONVOLUTIONAL NOISE
M. J. F. Gales and S. J. Young
This paper addresses the problem of speech recognition in the presence of both additive and convolutional noise. A new scheme is described, which is a simple extension to the standard Parallel Model Combination (PMC) technique. A modified `mismatch' function is introduced which accounts for the effects of convolutional noise. This `mismatch' function is then used to estimate the difference in channel conditions between training and test environments. Having estimated the tilt parameters, Maximum Likelihood (ML) estimates of the corrupted speech model may be obtained. The scheme is evaluated using the NOISEX-92 database. The performance in the presence of both interfering additive noise and convolutional noise shows only slight degradation compared with that obtained when no convolutional noise is present.
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